Department of Construction, School of Technology, University of Extremadura, Avda. de la Universidad s/n, Cáceres, Spain.
Center of Allergy & Environment (ZAUM), Member of the German Center for Lung Research (DZL), Technical University and Helmholtz Center Munich, Munich, Germany.
Sci Total Environ. 2020 Sep 20;736:139363. doi: 10.1016/j.scitotenv.2020.139363. Epub 2020 May 25.
Bioinformatics clustering application for mining of a large set of olive pollen aerobiological data to describe the daily distribution of Olea pollen concentration. The study was performed with hourly pollen concentrations measured during 8 years (2011-2018) in Extremadura (Spain). Olea pollen season by quartiles of the pollen integral in preseason (Q1: 0%-25%), in-season (Q2 and Q3: 25%-75%) and postseason (Q4: 75%-100%). Days with pollen concentrations above 100 grains/m were clustered according to the daily distribution of the concentrations. The factors affecting the prevalence of the different clusters were analyzed: distance to olive groves and the moment during the pollen season and the meteorology. During the season, the highest hourly concentrations during the day where between 12:00 and 14:00, while during the preseason the highest hourly concentrations were detected in the afternoon and evening hours. In the postseason the pollen concentrations were more homogeneously distributed during 9-16 h. The representation shows a well-defined hourly pattern during the season, but a more heterogeneous distribution during the preseason and postseason. The cluster dendrogram shows that all the days could be clustered in 6 groups: most of the clusters shows the daily peaks between 11:00 and 15:00 with a smooth curve (Cluster 1 and 3) or with a strong peak (2 and 5). Days included in cluster 9 shows an earlier peak in the morning (before 9:00). On the other hand, cluster 6 shows a peak in the afternoon, after 15:00. Hourly concentrations show a sharper pattern during the season, with the peak during the hours close to the emission. Out of the season, when pollen is expected to come from farther distances, the hourly peak is located later from the emission time of the trees. Significant factors for predicting the hourly pattern were wind speed and direction and the distance to the olive groves.
生物信息学聚类应用程序，用于挖掘大量的橄榄花粉气传数据，以描述橄榄花粉浓度的日分布。该研究是在西班牙埃斯特雷马杜拉地区（Extremadura）进行的，使用 8 年（2011-2018 年）每小时测量的花粉浓度进行。按照 preseason（Q1：0%-25%）、季节内（Q2 和 Q3：25%-75%）和季后（Q4：75%-100%）花粉积分的四分位数对橄榄花粉季节进行划分。根据浓度的日分布对花粉浓度高于 100 粒/米的天数进行聚类。分析了影响不同聚类出现的因素：到橄榄林的距离、花粉季节的时间以及气象条件。在季节内，白天最高的每小时浓度出现在 12:00 至 14:00 之间，而在 preseason 期间，最高的每小时浓度出现在下午和傍晚。在季后，花粉浓度在 9-16 小时期间分布更加均匀。该表示显示了季节内的明确的每小时模式，但在 preseason 和季后分布更加不均匀。聚类树状图显示，所有的天数都可以分为 6 组：大多数聚类显示出每日高峰出现在 11:00 至 15:00 之间，曲线较为平滑（聚类 1 和 3）或有强烈的峰值（聚类 2 和 5）。包含在聚类 9 中的天数显示出早晨（9:00 之前）的较早峰值。另一方面，聚类 6 显示出下午 15:00 后的峰值。季节内的每小时浓度模式更加明显，峰值出现在树木排放后的几个小时内。不在季节内时，花粉预计来自更远的距离，因此每小时的峰值出现在树木排放时间之后。预测每小时模式的重要因素是风速和风向以及与橄榄林的距离。
